Development of a Physiologically Based Pharmacokinetic Population Model for Diabetic Patients and its Application to Understand Disease-drug–drug Interactions

• Published in: Clinical pharmacokinetics Vol. 63; no. 6; pp. 831 - 845
• Main Authors: Li, Yafen, Li, Xiaonan, Zhu, Miao, Liu, Huan, Lei, Zihan, Yao, Xueting, Liu, Dongyang
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2024; Springer Nature B.V
• Subjects: Adult; Age; Aged; Body mass index; Computer Simulation; Cytochrome; Cytochrome P-450 Enzyme System - metabolism; Diabetes; Diabetes Mellitus - drug therapy; Disease; Drug dosages; Drug Interactions; Enzymes; Female; Humans; Hyperglycemia; Hypoglycemic Agents - administration & dosage; Hypoglycemic Agents - pharmacokinetics; Internal Medicine; Male; Mathematical functions; Medicine; Medicine & Public Health; Middle Aged; Models, Biological; Motility; Original Research Article; Pharmacodynamics; Pharmacokinetics; Pharmacology/Toxicology; Pharmacotherapy; Physiology; Population; White people
• ISSN: 0312-5963; 1179-1926; 1179-1926
• DOI: 10.1007/s40262-024-01383-2

Introduction The activity changes of cytochrome P450 (CYP450) enzymes, along with the complicated medication scenarios in diabetes mellitus (DM) patients, result in the unanticipated pharmacokinetics (PK), pharmacodynamics (PD), and drug–drug interactions (DDIs). Physiologically based pharmacokinetic (PBPK) modeling has been a useful tool for assessing the influence of disease status on CYP enzymes and the resulting DDIs. This work aims to develop a novel diabetic PBPK population model to facilitate the prediction of PK and DDI in DM patients. Methods First, mathematical functions were constructed to describe the demographic and non-CYP physiological characteristics specific to DM, which were then incorporated into the PBPK model to quantify the net changes in CYP enzyme activities by comparing the PK of CYP probe drugs in DM versus non-DM subjects. Results The results show that the enzyme activity is reduced by 32.3% for CYP3A4/5, 39.1% for CYP2C19, and 27% for CYP2B6, while CYP2C9 activity is enhanced by 38% under DM condition. Finally, the diabetic PBPK model was developed through integrating the DM-specific CYP activities and other parameters and was further used to perform PK simulations under 12 drug combination scenarios, among which 3 combinations were predicted to result in significant PK changes in DM, which may cause DDI risks in DM patients. Conclusions The PBPK modeling applied herein provides a quantitative tool to assess the impact of disease factors on relevant enzyme pathways and potential disease-drug–drug-interactions (DDDIs), which may be useful for dosing regimen optimization and minimizing the DDI risks associated with the treatment of DM. Graphical Abstract